結論與建議

本章分為二節，第一節為本研究之結論，第二節就本研究未盡完備之處，提 出一些研究建議，供後續研究者參考。茲分述如下：

第一節 結論

實驗一中，受試者能力真值呈現常態與雙峰分布時，藉由加入新試題來估計 其能力值，除了可以估計出新增試題參數之參數值外，更可估計出更精準的受試 者的能力值估計值。故可推論在進行

但若受試者能力真值呈現偏態分布時，無法估算出較精準新增試題之參數與 受試者能力值。

實驗二中，因所採用題庫試題的試題參數值非是初始真值，而是試題參數估 計值，雖較接近真實施測情境，估計之結果均較實驗一來得高，仍與實驗一一樣 在受試者能力真值呈現常態與雙峰分布時，藉由加入新試題來估計其能力值，可 以估計出新增試題參數之參數與更精準的受試者的能力值估計值。受試者能力真 值在呈現偏態分布時，亦無法估算出較精準試題之參數與受試者能力值。

第二節 建議

茲就本研究未盡完備之處，提出一些研究建議，供後續研究者參考。

一、本研究在加入新增試題估計時採用順序分配給題的方式，並未考量依受試者 能力估計值給題，故之後的研究還可以進一步探討加入新試題的方式。

二、本研究在選題方法上未考慮試題曝光率之控管，後續研究可深入探討加入曝 光率之控管後的成效。

三、本研究探討了受試者三種不同能力分布的情況，但在偏態分布中只模擬負偏 態分布，未考慮正偏態分布情形，故之後的研究還可以進一步探討正偏態的

參考文獻

中文部分

王寶墉(1995)。現代測驗理論。台北市：心理出版社。

方秀惠(2002)。題庫難度與先前能力分配對適性測驗效率之研究。國立高雄師範 大學資訊教育研究所碩士論文，未出版。

余民寧(1992)。試題反應的介紹(十二)-電腦化適性測驗。研習資訊，10(5)，5-9。

李茂能(2000)。中文電腦化適性測驗系統之應用與評鑑。台北市：文景。

張雅媛(2007)。融合 kernel smoothing 之 MMLE 法於 IRT 參數估計之應用。

國立台中教育大學教育測驗統計研究所碩士論文，未出版。

英文部分

Ban, J.C., Hanson, B.A, Yi, Q., & Harris, D.J.(2001)

Data Sparseness and Online Pretest Item Calibration/Scaling Methods in CAT

. Annual Meeting of the American Educational Research Association.

Birnbaum, A. (1968).

Some latent trait models and their use in inferring an examinee’s ability.

In F. M. Lord & M. R. Novick, Statistical theories of mental test scores (chapters 17-20). Reading, MA: Addison-Wesley.

Bock, R. D. & Lieberman, M (1970) Fitting a response model for n dichotomously scored items.

Psychometrika

, 35, 179-197.

Bock, R. D. & Aitkin, M (1981) Marginal maximum likelihood estimation of item parameters：Application of an EM algorithm.

Psychometrika

, 46, 443-459.

Baker, F. B. (2004).

Item Response Theory ： Parameter estimation techniques

. New York：Marcel Dekker.

DeMars, C.E.(2005)

"Guessing" Parameter Estimates for Multidimensional

IRT Models.

American Educational Research Association,

Donoghue, J.R. & Isham,S.P. (1996)

Comparing the Effectiveness of Procedures to Detect Item Parameter Drift

. Educational Testing Service.

Gao.F & Lisue.C (2005) Bayesian or Non-Bayesian:A Comparison Study of Item Parameter Estimation in the Three-Parameter Logistic Model.

Applied Measurement in Education

, 18(4), 351–380

Glas .C. A.W. & Hendrawan.I (2005).

Testing Linear Models for Ability Parameters in Item Response Models

. MULTIVARIATE BEHAVIORAL RESEARCH, 40(1), 25–51

Glas, C. A. W., & van der Linden, W. J. (2006).

Modeling Variability in Item Parameters in Educational Measurement

. Law School Admission Council Computerized Testing Report 01-07.

Jones,D.H & Nediak.M (2000)

Item Parameter Calibration of LSAT Items Using MCMC Approximation of Bayes Posterior Distributions

. Law School Admission Council Computerized Testing Report 00-05

Lindley, D.V.(1971)

Bayesian statistics: A review

. Philadelphia: Society for Industrial and Applied Mathetics.

Lord, F. M. (1977).

Practical applications of item characteristic curve theory.

Jaurnal of educational Measurement,

14, 117-138.

Lord, F. M. (1980).

Applications of item response theory to practical testing problems.

Hillsdale, NJ: Lawrence Eribaum Associates.

Millman, J. & Arter, J. A. (1984). Issues in item banking.

Journal of

Educational Measurement

, 21, 315-330.

Psychometrica

, 51,177-195.

Mislevy, R.J. & Stocking, M.L. (1989). A consumer’s Guide to LOGIST and BILOG..

Applied Psychological Measurement

,13,57-75.

Mislevy, R. J. & Bock, R. D. (1982). Implementation of the EM algorithm in the estimation of item parameters: The BILOG computer program.

Item Response Theory and Computerized Adaptive Testing Conference Proceedings

.

Ree, M. J. (1981).

The effects of item calibrations, sample size, and item pool size on adaptive testing. Applied Psychological Measurement

, 5, 11-19.

Stocking, M.L. (1994).

Three practical issues for modern adaptive testing item pools.

Educational Testing Service, Princeton, N.J. (ERIC Document Reproduction Service No. ED 385 551)

Swaminathan. H., & Gifford, J.A. (1982) Bayesian estimation in the Rasch model.

Journal of Educational Statistics

, 7, 175-191.

Tang,K.L. & Eignor.D.R (2001)

A Study of the Use of Collateral Statistical Information in Attempting to Reduce TOEFL IRT Item Parameter Estimation Sample Sizes

. TOFEL Technique Report 17.

Wang, T., & Vispoel, W. P.(1998).

Properties of ability estimation methods in computerized adaptive testing. Journal of Educational Measurement,

35,109-135.

Wolfgang, H. & Marlene, M. (2004)

Nonparametric and Semiparametric Models.

Heidelberge New York.

Yamamoto.K (1995)

Estimating the Effects of Test Length and Test Time

on Parameter Estimation Using the HYERID Model.

TOFEL

Technique Report 10.

Yao,L, Patz,R.J. & Hanson,B.A. (2002)

More Effcient Markov Chain Monte Carlo Estimation in IRT Using Marginal Posteriors

. National Council on Measurement in Education.

Zimowski, M. F., Muraki, E. , Mislevy, R. J. & Bock, R.D. (1996).

BILOG-MG.

Scientific Software lnternational.

在文檔中 電腦化適性測驗題庫擴增研究 (頁 61-65)